Work

CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

Year: 2020

Type: article

Abstract: An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.

Source: Genome biology

DOI: 10.1186/s13059-020-02054-8

Authors: Zijian Ni, Shuyang Chen, Jared Brown, Christina Kendziorski

Institution: University of Wisconsin–Madison

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,990

Funders: National Institutes of Health, National Institute of General Medical Sciences